Vt
From Genome Analysis Wiki
Introduction
vt is a variant tool set that discovers short variants from Next Generation Sequencing data. The features are being rolled out to github as major rewriting is being undertaken.
Installation
The source files are housed in github. htslib is used and a copy of a developmental freeze is stored as part of the vt repository to ensure compatibility.
To install, perform the following steps:
#this will create a directory named vt in the directory you cloned the repository 1. git clone https://github.com/atks/vt.git #change directory to vt 2. cd vt #run make, note that compilers need to support the c++0x standard 3. make
Building has been tested on Linux and Mac systems on gcc 4.8.1 and clang 3.4.
Updating
vt is currently under heavy development, you will probably need to update often.
#remove all object files #you need to do this as source files for the static libraries might have changed and need to be removed. 1. make clean
#update source files 2. git pull
#compile and link, the -j option tells Makefile to run up to 40 independent commands in parallel 3. make -j 40
General Features
Common options
-i multiple intervals in <seq>:<start>-<end> format delimited by commas.
-I multiple intervals in <seq>:<start>-<end> format listed in a text file line by line.
-o defines the out file which and has the STDOUT set as the default.
You may modify the STDOUT to output the binary version of the format. Uncompressed
VCF and BCF streams are indicated by - and + respectively.
-f filter expression
-s sequential region selection as opposed to random access of regions specified by the i option.
This is useful when you want to select many close-by regions, while the -i option works,
it is less efficient and also selects a variant multiple times if it overlaps 2 regions. This
option iterates through the variants in the file sequentially and checks for overlap with the
bed file given.
Uncompressed BCF streams
htslib is designed with BCF as the underlying data structure and it has incorporated awareness of uncompressed BCF streams in the i/o API. One may use this feature to stream uncompressed BCF records to save on computational time spent on (de)compression.
#using textual VCF streams indicated by - cat view -h mills.vcf | vt normalize - -r hs37d5.fa | vt mergedups - -o out.bcf
#using uncompressed BCF streams indicated by + cat mills.vcf | vt normalize - -r hs37d5.fa -o + | vt mergedups + -o out.bcf
In this example, the former took 0.84s while the latter took 0.64s to process. (24% speed up!)
Filters
For some programs. you may define a filter via the -f option.
This allows you to only analyse biallelic indels that are passed on chromosome 20. vt profile_na12878 vt.bcf -g na12878.reference.txt -r genome.fa -f "N_ALLELE==2&&VTYPE==INDEL&&PASS" -i 20
Other examples of filters
#all variants with a SNP in them VTYPE&SNP #Simple insertions of length 1 VTYPE==INDEL&&DLEN==1 #Indels of length 1 VTYPE==INDEL&&LEN==1
Variant characteristics VTYPE,N_ALLELE,DLEN,LEN
Variant value types SNP,MNP,INDEL,CLUMPED
Biallelic SNPs only : VTYPE==SNP&&N_ALLELE==2 Biallelic Indels with embedded SNP : VTYPE==(SNP|INDEL)&&N_ALLELE==2 Biallelic variants involving insertions : VTYPE&INDEL&&DLEN>0&&N_ALLELE==2 Biallelic variants involving 1bp variants : LEN==1&&N_ALLELE==2
FILTER fields PASS, FILTER.<tag>
INFO fields INFO.<tag>
Passed biallelic SNPs only : PASS&&VTYPE==SNP&&N_ALLELE==2 Passed Common biallelic SNPs only : PASS&&VTYPE==SNP&&N_ALLELE==2&&INFO.AF>0.005 Passed Common biallelic SNPs or rare indels : (PASS&&VTYPE==SNP&&N_ALLELE==2&&INFO.AF>0.005)||(VTYPE&INDEL&&INFO.AF<=0.005)
Operations ==,~,&&,||,&,|,+,-,*,/
Failed rare variants : ~PASS&&(INFO.AC/INFO.AN<0.005)
The motivation in this case is because Indels are a heterogeneous set of variants and thus we usually examine them from many different characteristics. The following programs support filter support.
- peek
- view
- concat
- profile_snps
- profile_indels
- profile_na12878
- profile_mendelian
- profile_len
- profile_chrom
- profile_afs
- profile_hwe
- concordance
- partition
Alternate headers
As BCF is a restrictive format of VCF where all meta data must be present in the header, vt provides a mechanism to read an alternative header for VCF files that do not have a complete header. Simply provide a header file stub named as <vcf-file>.hdr and vt will automatically read it instead of the original header in <vcf-file>.
VCF Manipulation
View
Views a VCF or VCF.GZ or BCF file.
#views mills.bcf and outputs to standard out vt view -h mills.bcf #views mills.bcf and locally sorts it in a 10000bp window and outputs to out.bcf vt view -h -w 10000 mills.bcf
usage : vt view [options] <in.vcf>
options : -o output VCF/VCF.GZ/BCF file [-]
-w local sorting window size [0]
-s print site information only without genotypes [false]
-h print header [false]
-p print options and summary []
-I file containing list of intervals []
-i intervals []
-? displays help
Index
Indexes a VCF.GZ or BCF file.
#indexes mills.bcf vt index mills.bcf
usage : vt index [options] <in.vcf>
options : -p print options and summary []
-- ignores the rest of the labeled arguments following this flag
-h displays help
Sorting
Local sorting can be done using view setting the -w option to a non 0 value.
You will want to locally sort a VCF file if you are aware that the records are not in order in short stretches. This will not work if the records are not in chromosomal order. The window dictates the size of the region to buffer the records while sorting.
Normalization
Normalize variants in a VCF file. Normalized variants may have their positions changed; in such cases, the normalized variants are reordered and output in an ordered fashion. The local reordering takes place over a window of 10000 base pairs.
#normalize variants and write out to dbsnp.normalized.vcf vt normalize mdbsnp.vcf -r seq.fa -o dbsnp.normalized.vcf
#normalize variants, send to standard out and remove duplicates. vt normalize dbsnp.vcf -r seq.fa | vt mergedups - -o dbsnp.normalized.merged.vcf
#variants that are normalized will be annotated with an OLD_VARIANT info tag. #CHROM POS ID REF ALT QUAL FILTER INFO 19 29238772 . C G . PASS VT=SNP;OLD_VARIANT=19:29238771:TC/TG 20 60674709 . GCCCAGCCCCAC G . PASS VT=INDEL;OLD_VARIANT=20:60674718:CACCCCAGCCCC/C
#this shows a sample output with the normalization operations that were used #categorized into 5 categories each for biallelic and multiallelic variants.
stats: biallelic no. left trimmed : 156908 no. right trimmed : 323 no. left and right trimmed : 33 no. right trimmed and left aligned : 7 no. left aligned : 12360
total no. biallelic normalized : 169631
multiallelic no. left trimmed : 627189 no. right trimmed : 2509 no. left and right trimmed : 1498 no. right trimmed and left aligned : 212 no. left aligned : 1783
total no. multiallelic normalized : 633191
total no. variants normalized : 802822 total no. variants observed : 88052639
usage : vt normalize [options] <in.vcf>
options : -o output VCF file [-]
-I file containing list of intervals []
-i intervals []
-r reference sequence fasta file []
-- ignores the rest of the labeled arguments following this flag
-h displays help
Decompose blocksub
Decomposes biallelic block substitutions into its constituent SNPs.
#decomposes multiallelic variants into biallelic variants and write out to gatk.decomposed.vcf vt decompose gatk.vcf -o gatk.decomposed.vcf
#before decomposition #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT S1 20 763837 . CA TG 50340.1 PASS AC=1;AN=2 GT 0|1
#after decomposition #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT S1 20 763837 . C T 50340.1 PASS AC=1;AN=2;OLD_CLUMPED=20:763837:CA/TG GT 0|1 20 763838 . A G 50340.1 PASS AC=1;AN=2;OLD_CLUMPED=20:763837:CA/TG GT 0|1 One might want to post process the partial genotypes like 1/. to the best guess genotype based on the PL values.
description : decomposes multialleic variants into biallelic in a VCF file.
usage : vt decompose [options] <in.vcf>
options : -o output VCF file [-] -I file containing list of intervals [] -i intervals [] -? displays help
Decompose
Decompose multiallelic variants in a VCF file. If the VCF file has genotype fields GT,PL or GL, they are modified to reflect the change in alleles. All other genotype fields are removed.
#decomposes multiallelic variants into biallelic variants and write out to gatk.decomposed.vcf vt decompose gatk.vcf -o gatk.decomposed.vcf
#before decomposition #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT S1 S2 1 3759889 . TA TAA,TAAA,T . PASS AF=0.342,0.173,0.037 GT:DP:PL 1/2:81:281,5,9,58,0,115,338,46,116,809 0/0:86:0,30,323,31,365,483,38,291,325,567
#after decomposition #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT S1 S2 1 3759889 . TA TAA . PASS AF=0.342,0.173,0.037;OLD_MULTIALLELIC=1:3759889:TA/TAA/TAAA/T GT:PL 1/.:281,5,9 0/0:0,30,323 1 3759889 . TA TAAA . . OLD_MULTIALLELIC=1:3759889:TA/TAA/TAAA/T GT:PL ./1:281,58,115 0/0:0,31,483 1 3759889 . TA T . . OLD_MULTIALLELIC=1:3759889:TA/TAA/TAAA/T GT:PL ./.:281,338,809 0/0:0,38,567
One might want to post process the partial genotypes like 1/. to the best guess genotype based on the PL values.
description : decomposes multialleic variants into biallelic in a VCF file.
usage : vt decompose [options] <in.vcf>
options : -o output VCF file [-] -I file containing list of intervals [] -i intervals [] -? displays help
Merge duplicate variants
Merges duplicate variants by position with the option of considering alleles. (This just discards the duplicate variant that appears later in the VCF file)
#merge duplicate variants and save output in mills.merged.vcf vt mergedups mills.vcf -o mills.merged.vcf
usage : vt mergedups [options] <in.vcf>
options : -o output VCF file [-]
-p merge by position [false]
Concatenate
Concatenate VCF files. Assumes individuals are in the same order and files share the same header.
#concatenate chr1.mills.bcf and chr2.mills.bcf vt concat chr1.mills.bcf chr2.mills.bcf -o mills.bcf
usage : vt concat [options] <in1.vcf>...
options : -s print site information only without genotypes [false]
-p print options and summary []
-L file containing list of input VCF files
-o output VCF file [-]
-I file containing list of intervals []
-i intervals
-? displays help
VCF Inspection and Evaluation
Peek
Summarizes the variants in a VCF file
#summarizes the variants found in mills.vcf vt peek mills.vcf
usage : vt peek [options] <in.vcf>
options : -o output VCF file [-]
-I file containing list of intervals []
-i intervals []
-r reference sequence fasta file []
-- ignores the rest of the labeled arguments following this flag
-h displays help
For a more detailed guide on variant classification.
#This is a sample output of a peek command which summarizes the variants found in a VCF file.
stats: no. of samples : 0
no. of chromosomes : 22
========== Micro variants ==========
no. of SNPs : 77228885
2 alleles (ts/tv) : 77011302 (2.11) [52287790/24723512]
3 alleles (ts/tv) : 216560 (0.75) [185520/247600]
4 alleles (ts/tv) : 1023 (0.50) [1023/2046]
no. of MNPs : 0
2 alleles (ts/tv) : 0 (-nan) [0/0]
>=3 alleles (ts/tv) : 0 (-nan) [0/0]
no. Indels : 2147564
2 alleles (ins/del) : 2124842 (0.47) [683250/1441592]
>=3 alleles (ins/del) : 22722 (2.12) [32411/15286]
no. SNP/MNP : 0
3 alleles (ts/tv) : 0 (-nan) [0/0]
>=4 alleles (ts/tv) : 0 (-nan) [0/0]
no. SNP/Indels : 12913
2 alleles (ts/tv) (ins/del) : 412 (0.41) [120/292] (3.68) [324/88]
>=3 alleles (ts/tv) (ins/del) : 12501 (0.43) [7670/17649] (18.64) [12434/667]
no. MNP/Indels : 153
2 alleles (ts/tv) (ins/del) : 0 (-nan) [0/0] (-nan) [0/0]
>=3 alleles (ts/tv) (ins/del) : 153 (0.30) [138/465] (0.27) [67/248]
no. SNP/MNP/Indels : 2
3 alleles (ts/tv) (ins/del) : 0 (-nan) [0/0] (-nan) [0/0]
4 alleles (ts/tv) (ins/del) : 2 (0.00) [3/5] (1.00) [3/3]
>=5 alleles (ts/tv) (ins/del) : 0 (-nan) [0/0] (-nan) [0/0]
no. of clumped variants : 19025
2 alleles : 0 (-nan) [0/0] (-nan) [0/0]
3 alleles : 18508 (0.16) [12152/75366] (0.00) [93/18653]
4 alleles : 451 (0.15) [369/2390] (0.33) [201/609]
>=5 alleles : 66 (0.09) [37/414] (1.19) [107/90]
====== Other useful categories =====
no. complex variants : 32093
2 alleles (ts/tv) (ins/del) : 412 (0.41) [120/292] (3.68) [324/88]
>=3 alleles (ts/tv) (ins/del) : 31681 (0.21) [20369/96289] (0.64) [12905/20270]
======= Structural variants ========
no. of structural variants : 41217
2 alleles : 38079
deletion : 13135
insertion : 16451
mobile element : 16253
ALU : 12513
LINE1 : 2911
SVA : 829
numt : 198
duplication : 664
inversion : 100
copy number variation : 7729
>=3 alleles : 3138
copy number variation : 3138
========= General summary ==========
no. of reference : 0
no. of observed variants : 79449759
no. of unclassified variants : 0
Partition
Partitions variants between 2 indexed VCF files.
#partitions all variants in bi1.bcf and bi2.bcf vt partition bi1.bcf bi2.bcf
Options: input VCF file a bi1.bcf
input VCF file b bi2.bcf
A: 504676 variants
B: 1389333 variants
ts/tv ins/del
A-B 37564 [0.19] [1.34]
A&B 467112 [1.55] [0.72]
B-A 922221 [1.20] [0.58]
of A 92.6%
of B 33.6%
#partitions only passed variants in bi1.bcf and bi2.bcf vt partition bi1.bcf bi2.bcf -f PASS
Options: input VCF file a bi1.bcf
input VCF file b bi2.bcf
[f] filter PASS
A: 466148 variants
B: 986056 variants
ts/tv ins/del
A-B 47261 [0.44] [1.36]
A&B 418887 [1.80] [0.68]
B-A 567169 [1.43] [0.72]
of A 89.9%
of B 42.5%
partition v0.5
description : partition variants. check the overlap of variants between 2 data sets.
usage : vt partition [options] <in1.vcf><in2.vcf>
options : -f filter
-I file containing list of intervals []
-i intervals []
-? displays help
Annotate Variants
Annotates variants in a VCF file. The GENCODE annotation file should be bgzipped and indexed with tabix. This is available in the vt resource bundle.
#annotates the variants found in mills.vcf vt annotate_variants mills.vcf -r hs37d5.fa -g gencode.v19.annotation.gtf.gz
#annotates variants with the following fields ##INFO=<ID=VT,Number=1,Type=String,Description="Variant Type - SNP, MNP, INDEL, CLUMPED"> ##INFO=<ID=GENCODE_FS,Number=0,Type=Flag,Description="Frameshift INDEL"> ##INFO=<ID=GENCODE_NFS,Number=0,Type=Flag,Description="Non Frameshift INDEL">
usage : vt annotate_variants [options] <in.vcf>
options : -g GENCODE annotations GTF file []
-r reference sequence fasta file []
-o output VCF file [-]
-I file containing list of intervals []
-i intervals
-? displays help
Compute Features
Compute features in a VCF file. Example of statistics are Allele counts, Genotype Likelihood based Inbreeding Coefficient. Hardy-Weinberg Genotype Likelihood based Allele Frequencies
#compute features for the variants found in vt.vcf #requires GT, PL and DP vt compute_features vt.vcf
#annotates variants with the following fields ##INFO=<ID=AC,Number=A,Type=Integer,Description="Alternate Allele Counts"> ##INFO=<ID=AN,Number=1,Type=Integer,Description="Total Number Allele Counts"> ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data"> ##INFO=<ID=AF,Number=A,Type=Float,Description="Alternate Allele Frequency"> ##INFO=<ID=GC,Number=G,Type=Integer,Description="Genotype Counts"> ##INFO=<ID=GN,Number=1,Type=Integer,Description="Total Number of Genotypes Counts"> ##INFO=<ID=GF,Number=G,Type=Float,Description="Genotype Frequency"> ##INFO=<ID=HWEAF,Number=A,Type=Float,Description="Genotype likelihood based MLE Allele Frequency assuming HWE"> ##INFO=<ID=HWEGF,Number=G,Type=Float,Description="Genotype likelihood based MLE Genotype Frequency assuming HWE"> ##INFO=<ID=MLEAF,Number=A,Type=Float,Description="Genotype likelihood based MLE Allele Frequency"> ##INFO=<ID=MLEGF,Number=G,Type=Float,Description="Genotype likelihood based MLE Genotype Frequency"> ##INFO=<ID=HWE_LLR,Number=1,Type=Float,Description="Genotype likelihood based Hardy Weinberg ln(Likelihood Ratio)"> ##INFO=<ID=HWE_LPVAL,Number=1,Type=Float,Description="Genotype likelihood based Hardy Weinberg Likelihood Ratio Test Statistic ln(p-value)"> ##INFO=<ID=HWE_DF,Number=1,Type=Integer,Description="Degrees of freedom for Genotype likelihood based Hardy Weinberg Likelihood Ratio Test Statistic"> ##INFO=<ID=FIC,Number=1,Type=Float,Description="Genotype likelihood based Inbreeding Coefficient"> ##INFO=<ID=AB,Number=1,Type=Float,Description="Genotype likelihood based Allele Balance">
usage : vt compute_features for variants [options] <in.vcf>
options : -s print site information only without genotypes [false]
-o output VCF/VCF.GZ/BCF file [-]
-f filter expression []
-I File containing list of intervals
-i Intervals
-? displays help
Profile Indels
Profile Indels. The reference data sets can be obtained from vt resource bundle.
#profile indels found in mills.vcf vt profile_indels -g indel.reference.txt mills.vcf -r hs37d5.fa -i 20
#this is a sample output for indel profiling.
# square brackets contain the ins/del ratio.
# for the FS/NFS field, that is the proportion of coding indels that are frame shifted.
# The numbers in curved bracket are the counts of frame shift and non frame shift indels respectively.
data set
No Indels : 46974 [0.89]
FS/NFS : 0.26 (8/23)
dbsnp
A-B 30704 [0.92]
A&B 16270 [0.83]
B-A 2049488 [1.52]
Precision 34.6%
Sensitivity 0.8%
mills
A-B 43234 [0.88]
A&B 3740 [1.00]
B-A 203278 [0.98]
Precision 8.0%
Sensitivity 1.8%
mills.chip
A-B 46847 [0.89]
A&B 127 [0.90]
B-A 8777 [0.93]
Precision 0.3%
Sensitivity 1.4%
affy.exome.chip
A-B 46911 [0.89]
A&B 63 [0.43]
B-A 33997 [0.47]
Precision 0.1%
Sensitivity 0.2%
# This file contains information on how to process reference data sets. # # dataset - name of data set, this label will be printed. # type - True Positives (TP) and False Positives (FP) # overlap percentages labeled as (Precision, Sensitivity) and (False Discovery Rate, Type I Error) respectively # - annotation # file is used for GENCODE annotation of frame shift and non frame shift Indels # filter - filter applied to variants for this particular data set # path - path of indexed BCF file #dataset type filter path dbsnp TP INDEL dbsnp.xxsnps_indels.sites.bcf mills TP INDEL mills.208620indels.sites.bcf mills.chip TP INDEL mills.chip.158samples.8904indels.sites.bcf #mills.chip.common TP INDEL&&AF>0.005 grch37/mills.chip.158samples.8904indels.sites.bcf affy.exome.chip TP INDEL affy.exome.chip.1249samples.316520variants.sites.bcf #affy.exome.chip.poly TP INDEL&&AC!=0 grch37/affy.exome.chip.1249samples.316520variants.sites.bcf #affy.exome.chip.mono FP INDEL&&AC=0 grch37/affy.exome.chip.1249samples.316520variants.sites.bcf gencode.v19 annotation . gencode.v19.annotation.gtf.gz
usage : vt profile_indels [options] <in.vcf>
options : -g file containing list of reference datasets []
-I file containing list of intervals []
-i intervals []
-r reference sequence fasta file []
-? displays help
Profile Mendelian Errors
Profile Mendelian errors
#profile mendelian errors found in vt.genotypes.bcf, generate tables in the directory mendel, requires pdflatex. vt profile_mendelian vt.genotypes.bcf -p trios.ped -x mendel
#this is a sample output for mendelian error profiling. #R and A stand for reference and alternate allele respectively. #Error% - mendelian error (confounded with de novo mutation) #HomHet - Homozygous-Heterozygous genotype ratios #Het% - proportion of hets Mendelian Errors
Father Mother R/R R/A A/A Error(%) HomHet Het(%) R/R R/R 14889 210 38 1.64 nan nan R/R R/A 3403 3497 74 1.06 0.97 50.68 R/R A/A 176 1482 155 18.26 nan nan R/A R/R 3665 3652 68 0.92 1.00 49.91 R/A R/A 1015 3151 990 0.00 0.64 61.11 R/A A/A 43 1300 1401 1.57 1.08 48.13 A/A R/R 172 1365 147 18.94 nan nan A/A R/A 47 1164 1183 1.96 1.02 49.60 A/A A/A 20 78 5637 1.71 nan nan
Parental R/R R/A A/A Error(%) HomHet Het(%) R/R R/R 14889 210 38 1.64 nan nan R/R R/A 7068 7149 142 0.99 0.99 50.28 R/R A/A 348 2847 302 18.59 nan nan R/A R/A 1015 3151 990 0.00 0.64 61.11 R/A A/A 90 2464 2584 1.75 1.05 48.81 A/A A/A 20 78 5637 1.71 nan nan
Parental R/R R/A A/A Error(%) HomHet Het(%) HOM HOM 14909 288 5675 1.66 nan nan HOM HET 7158 9613 2726 1.19 1.00 49.90 HET HET 1015 3151 990 0.00 0.64 61.11 HOMREF HOMALT 348 2847 302 18.59 nan nan
total mendelian error : 2.505% no. of trios : 2 no. of variants : 25346
profile_mendelian v0.5
usage : vt profile_mendelian [options] <in.vcf>
options : -q minimum genotype quality
-d minimum depth
-r reference sequence fasta file []
-x output latex directory []
-p pedigree file
-I file containing list of intervals []
-i intervals
-? displays help
Profile NA12878
Profile Mendelian errors
#profile NA12878 overlap with broad knowledgebase and illumina platinum genomes for the file vt.genotypes.bcf for chromosome 20. vt profile_na12878 vt.genotypes.bcf -g na12878.reference.txt -r hs37d5.fa -i 20
#this is a sample output for mendelian error profiling.
#R and A stand for reference and alternate allele respectively.
#Error% - mendelian error (confounded with de novo mutation)
#HomHet - Homozygous-Heterozygous genotype ratios
#Het% - proportion of hets
data set
No Indels : 27770 [0.94]
FS/NFS : 0.26 (8/23)
broad.kb
A-B 13071 [1.19]
A&B 14699 [0.76]
B-A 21546 [0.62]
Precision 52.9%
Sensitivity 40.6%
illumina.platinum
A-B 17952 [0.88]
A&B 9818 [1.07]
B-A 2418 [0.88]
Precision 35.4%
Sensitivity 80.2%
broad.kb
R/R R/A A/A ./.
R/R 346 145 3 5473
R/A 3 4133 9 758
A/A 2 136 2186 956
./. 2 139 86 322
Total genotype pairs : 6963
Concordance : 95.72% (6665)
Discordance : 4.28% (298)
illumina.platinum
R/R R/A A/A ./.
R/R 1768 85 2 0
R/A 10 4479 14 0
A/A 13 180 3028 0
./. 71 98 70 0
Total genotype pairs : 9579
Concordance : 96.83% (9275)
Discordance : 3.17% (304)
# This file contains information on how to process reference data sets. # # dataset - name of data set, this label will be printed. # type - True Positives (TP) and False Positives (FP) # overlap percentages labeled as (Precision, Sensitivity) and (False Discovery Rate, Type I Error) respectively # - annotation # file is used for GENCODE annotation of frame shift and non frame shift Indels # filter - filter applied to variants for this particular data set # path - path of indexed BCF file #dataset type filter path broad.kb TP PASS /net/fantasia/home/atks/dev/vt/bundle/public/grch37/broad.kb.241365variants.genotypes.bcf illumina.platinum TP PASS /net/fantasia/home/atks/dev/vt/bundle/public/grch37/NA12878.illumina.platinum.5284448variants.genotypes.bcf #gencode.v19 annotation . /net/fantasia/home/atks/dev/vt/bundle/public/grch37/gencode.v19.annotation.gtf.gz
profile_na12878 v0.5
usage : vt profile_na12878 [options] <in.vcf>
options : -g file containing list of reference datasets []
-I file containing list of intervals []
-i intervals []
-r reference sequence fasta file []
-? displays help
Variant Calling
Discover
Discovers variants from reads in a BAM file.
#discover variants from NA12878.bam and write to stdout vt discover -b NA12878.bam -s NA12878 -r hs37d5.fa -i 20 -v snps,indels,mnps
usage : vt discover [options]
options : -b input BAM file
-v variant types [snps,mnps,indels]
-f fractional evidence cutoff for candidate allele [0.1]
-e evidence count cutoff for candidate allele [2]
-q base quality cutoff for bases [13]
-m MAPQ cutoff for alignments [20]
-s sample ID
-r reference sequence fasta file []
-o output VCF file [-]
-I file containing list of intervals []
-i intervals []
-- ignores the rest of the labeled arguments following this flag
-h displays help
Merge candidate variants
Merge candidate variants across samples. Each VCF file is required to have the FORMAT flags E and N and should have exactly one sample.
#merge candidate variants from VCFs in candidate.txt and output in candidate.sites.vcf vt merge_candidate_variants candidates.txt -o candidate.sites.vcf
usage : vt merge_candidate_variants [options]
options : -L file containing list of input VCF files
-o output VCF file [-]
-I file containing list of intervals []
-i intervals
-- ignores the rest of the labeled arguments following this flag
-h displays help
Construct Probes
Construct probes for genotyping a variant.
#construct probes from candidate.sites.bcf and output to standard out vt construct_probes candidates.sites.bcf -r ref.fa
usage : vt construct_probes [options] <in.vcf>
options : -o output VCF file [-]
-f minimum flank length [20]
-r reference sequence fasta file []
-I file containing list of intervals []
-i intervals []
-- ignores the rest of the labeled arguments following this flag
-h displays help
Genotype
Genotypes variants for each sample.
#genotypes variants found in candidate.sites.vcf from sample.bam vt genotype -r seq.fa -b sample.bam -i candidates.sites.vcf -o sample.sites.vcf
usage : vt genotype [options]
options : -r reference sequence fasta file []
-s sample ID []
-o output VCF file [-]
-b input BAM file []
-i input candidate VCF file []
-- ignores the rest of the labeled arguments following this flag
-h displays help
Resource Bundle
- External : resource bundle
- Internal : /net/fantasia/home/atks/ref/vt/grch37
Maintained by
This page is maintained by Adrian
